codegemma-2b
Maintainer: google
53
🛸
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
codegemma-2b is a 2 billion parameter text-to-text model from Google that specializes in code completion and generation tasks. It is part of the CodeGemma collection of open code models built on top of the larger Gemma model family. The codegemma-2b model is a faster, smaller variant compared to the larger codegemma-7b and codegemma-7b-it models, making it well-suited for quick code completion within code editors.
Model inputs and outputs
Inputs
- Code prefix and/or suffix: The model can take in partially completed code snippets to generate the missing middle portion.
- Natural language text or prompts: The model can also generate code from natural language descriptions or instructions.
Outputs
- Fill-in-the-middle code completion: The model can complete partially written code fragments.
- Generated code and text: For the instruction-tuned variants, the model can generate both code and natural language responses.
Capabilities
The codegemma-2b model is adept at code completion tasks, where it can fill in the middle of a partially written code snippet based on the surrounding context. It was trained using a "fill-in-the-middle" (FIM) objective, which teaches the model to generate the missing portion of code given the prefix and suffix.
The model can also generate code from natural language prompts, making it useful for tasks like prototyping new programs or translating high-level requirements into working code. While not as capable as the larger 7 billion parameter variants, the codegemma-2b model still demonstrates strong performance on coding benchmarks like HumanEval and MBPP.
What can I use it for?
The codegemma-2b model is well-suited for integration into code editors and IDEs to provide intelligent code completion suggestions. Developers can use it to speed up their coding workflow, improve code quality, and explore new programming ideas.
Beyond code completion, the model's natural language understanding capabilities make it useful for chatbots and virtual assistants that need to discuss or explain code. Educators could also leverage the model to create interactive coding learning experiences, providing feedback and suggestions to students as they write code.
Things to try
One interesting aspect of the codegemma-2b model is its ability to work with multiple files and code contexts. By using the <|file_separator|> token, you can provide the model with code snippets from different files or projects, which can help it generate more coherent and contextual completions.
Another thing to try is experimenting with different temperature and top-k/top-p settings during the generation process. Adjusting these parameters can allow you to control the level of creativity and diversity in the model's outputs, ranging from highly focused completions to more open-ended and exploratory code generation.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🔎
codegemma-7b
100
codegemma-7b is a 7 billion parameter text-to-text and text-to-code decoder-only model developed by Google. It is part of the CodeGemma collection of lightweight open code models built on top of the larger Gemma model family. codegemma-7b specializes in code completion and code generation tasks, providing high-quality code suggestions and text-to-code translation. This model can be contrasted with the codegemma-7b-it instruction-tuned variant, which is better suited for code chat and instruction following, and the codegemma-2b smaller model optimized for fast code completion. Model inputs and outputs Inputs Code Prefix and/or Suffix**: For code completion and generation, the model accepts a code fragment with a gap to be filled in. Natural Language Text**: The model can also accept natural language prompts to generate corresponding code. Outputs Code Completion**: For code completion, the model generates the missing code to fill in the gap. Code Generation**: Given a natural language prompt, the model can generate the corresponding code. Capabilities codegemma-7b excels at a variety of code-focused tasks. It can complete partially written code snippets, helping developers boost their productivity. The model also understands natural language instructions and can generate relevant code, making it useful for automating coding workflows or creating educational tools. Additionally, the model's text-to-text capabilities allow it to engage in natural conversations about code, programming concepts, and software development. What can I use it for? The codegemma-7b model has a wide range of potential applications. Developers can integrate it into their IDEs to provide intelligent code completion, accelerating the coding process. Educators can leverage the model to build interactive coding learning environments, where students can receive feedback and guidance. Companies building conversational AI assistants for software engineers can utilize codegemma-7b to power code-related dialogues and task automation. Things to try One interesting aspect of codegemma-7b is its ability to understand and generate code in the context of a broader conversation. Try providing the model with a natural language prompt about a programming problem, and see how it responds by generating relevant code snippets and explanations. You can also experiment with giving the model partially completed code and observe how it fills in the gaps, potentially suggesting alternative solutions or optimizations.
Updated Invalid Date
🌿
codegemma-1.1-7b-it
48
The codegemma-1.1-7b-it model is a large language model developed by Google that specializes in code-related tasks. It is part of the CodeGemma family of models, which are built on top of Google's Gemma base model. The codegemma-1.1-7b-it model is an instruction-tuned variant, designed for tasks like code generation, code chat, and instruction following. This model contrasts with the codegemma-7b and codegemma-2b models, which are pre-trained variants focused on code completion and generation. Model inputs and outputs Inputs Code prefix and/or suffix for code completion and generation scenarios Natural language text or prompts Outputs For pre-trained model variants: Fill-in-the-middle code completion, code, and natural language For instruction-tuned model variant: Code and natural language Capabilities The codegemma-1.1-7b-it model can be used for a variety of code-related tasks, including code completion, code generation from natural language, and code chat. It has been trained on a diverse dataset of code, web text, and mathematical content, allowing it to handle a wide range of programming languages and technical subjects. What can I use it for? The codegemma-1.1-7b-it model could be used to power interactive code learning experiences, aid in syntax correction, or provide coding practice. It could also be integrated into IDEs or other development tools to assist with code generation and exploration. Additionally, the model's ability to engage in code-focused conversations could be leveraged to build chatbots or virtual assistants for technical support or collaboration. Things to try One interesting aspect of the codegemma-1.1-7b-it model is its instruction-tuned nature, which allows it to follow natural language prompts and engage in interactive code-related tasks. You could try providing the model with open-ended prompts like "Write a Python function to calculate the nth Fibonacci number" and see how it responds. Additionally, you could experiment with using the model in a conversational setting, such as by asking it to explain a code snippet or provide suggestions for solving a programming problem.
Updated Invalid Date
👀
codegemma-7b-it
162
codegemma-7b-it is an instruction-tuned variant of the CodeGemma model, a collection of lightweight open code models built on top of Google's Gemma. This 7 billion parameter model specializes in code completion, code generation, code chat, and instruction following tasks. It can generate code from natural language prompts, answer questions about code fragments, and engage in conversations about programming and technical problems. Unlike the pre-trained CodeGemma 7B and CodeGemma 2B models, this instruction-tuned variant is designed for more open-ended interactions. Model inputs and outputs Inputs Text prompts that describe a task or request, such as "Write a Python function to calculate the nth Fibonacci number." Outputs Generated text that completes the requested task, such as a Python function to calculate Fibonacci numbers. Responses to questions or conversations about code and programming. Capabilities codegemma-7b-it can generate code in a variety of programming languages, including Python, Java, and JavaScript. It can also explain code, answer technical questions, and engage in open-ended conversations about programming concepts and problem-solving. The model's instruction-tuning allows for more flexible and contextual interactions compared to the pre-trained CodeGemma variants. What can I use it for? You can use codegemma-7b-it to automate code generation, build conversational programming assistants, or explore natural language interactions with code. For example, you could integrate it into an IDE to provide intelligent code completion, or build a chatbot that helps users debug issues or learn new programming skills. The model's small size also makes it suitable for deployment on edge devices or in resource-constrained environments. Things to try Try providing codegemma-7b-it with prompts that combine natural language and programming concepts, such as "Explain the difference between a stack and a queue in 50 words." The model's instruction-tuning allows it to engage in more nuanced and contextual exchanges beyond simple code generation. You can also experiment with fine-tuning the model on domain-specific datasets to further enhance its capabilities for your particular use case.
Updated Invalid Date
↗️
codegemma-7b-it-GGUF
47
The codegemma-7b-it-GGUF model is part of the CodeGemma collection of lightweight open code models built on top of Google's Gemma. The CodeGemma models are text-to-text and text-to-code decoder-only models available in different sizes and variants. The 7B parameter instruction-tuned variant, codegemma-7b-it-GGUF, is designed for code chat and instruction following tasks. Similar CodeGemma models include the codegemma-7b 7B parameter pretrained variant for code completion and generation, and the codegemma-2b 2B parameter pretrained variant for fast code completion. These models were developed by Google and are available through the Hugging Face platform. Model inputs and outputs Inputs For the pretrained model variants: code prefix and/or suffix for code completion and generation tasks, or natural language text or prompts For the instruction-tuned variant: natural language text or prompts Outputs For the pretrained model variants: fill-in-the-middle code completion, generated code, and natural language For the instruction-tuned variant: generated code and natural language responses Capabilities The codegemma-7b-it-GGUF model is capable of engaging in code-related conversations, generating code from natural language prompts, and following coding instructions. It can be used to power interactive code learning experiences, aid in syntax correction, or provide coding practice. What can I use it for? The CodeGemma models have a wide range of applications. The instruction-tuned codegemma-7b-it-GGUF variant could be used to build conversational interfaces that discuss code, generate code in response to natural language prompts, or support interactive code education experiences. The pretrained codegemma-7b and codegemma-2b variants could be integrated into IDEs to provide code completion and generation capabilities. Things to try One interesting aspect of the CodeGemma models is their use of the fill-in-the-middle (FIM) objective during training. This allows the models to work with both prefix-suffix-middle (PSM) and suffix-prefix-middle (SPM) modes, providing flexibility in how they can be used for code completion tasks. You can experiment with providing different configurations of prefix, suffix, and middle tokens to see how the model responds.
Updated Invalid Date